Artificial Intelligence Enabled Smart Monitoring and Controlling of IoT-Green House

Riskiawan, Hendra Yufit; Gupta, Nishu; Setyohadi, Dwi Putro Sarwo; Anwar, Saiful; Kurniasari, Arvita Agus; Hariono, Budi; Firmansyah, Muhammad Hafidh; Yogiswara, Y.; Mansur, Andi Besse Firdausiah; Basori, Ahmad Hoirul

doi:10.1007/s13369-023-07887-6

Cited by 4 publications

(8 citation statements)

References 33 publications

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“…CNNs leverage hierarchical layers of convolutional filters to extract intricate features from agricultural images, enabling precise identification of pests, diseases, and nutrient deficiencies. Other deep learning architectures, including recurrent neural networks (RNNs) and Long Short-Term Memory (LSTM) networks, are also utilized for tasks like time-series forecasting and crop yield prediction [114,206]. Other works utilized a hybrid of the neural network method and machine learning methods for detecting the freshness of fruit [207] and animal intrusion [191].…”

Section: Deep Learningmentioning

confidence: 99%

“…Unlike virtual resources, IoT devices cannot be effortlessly modified or managed remotely. This limitation can impede the smooth operation of AI/ML models on IoT platforms, occasionally necessitating physical intervention for maintenance or updates [206,228].…”

Section: Impact Of Iot Weaknesses On Ai/ml Modelsmentioning

confidence: 99%

“…The reviewed literature highlights various opportunities for advancing agricultural practices. Authors [76,206,234,235] emphasize the potential for enhancing crop-specific interventions, such as implementing dynamic nutrient delivery, as suggested by [206], and incorporating additional parameters influencing crops, as proposed by [234]. Additionally, expanding IoT applications along the coffee value chain, as recommended by [76], introduces possibilities for traceability and disease control.…”

Section: Agriculture Opportunitiesmentioning

confidence: 99%

“…Scaling IoT and AI/ML solutions for PA [131,206] remains a focal point for future research. Developing scalable models that can seamlessly integrate with existing agricultural practices while ensuring interoperability is pivotal for widespread adoption.…”

Section: Scalability and Interoperabilitymentioning

confidence: 99%

“…The integration of citizen-centric features in PA frameworks, as exemplified by [206], introduces a new dimension of community involvement. Exploring ways to enhance community participation in decision-making processes and leveraging collective intelligence can be a promising avenue.…”

Section: Citizen-centric Participationmentioning

confidence: 99%

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IoT Solutions with Artificial Intelligence Technologies for Precision Agriculture: Definitions, Applications, Challenges, and Opportunities

Senoo,

Anggraini,

Kumi

et al. 2024

Electronics

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The global agricultural sector confronts significant obstacles such as population growth, climate change, and natural disasters, which negatively impact food production and pose a threat to food security. In response to these challenges, the integration of IoT and AI technologies emerges as a promising solution, facilitating data-driven decision-making, optimizing resource allocation, and enhancing monitoring and control systems in agricultural operations to address these challenges and promote sustainable farming practices. This study examines the intersection of IoT and AI in precision agriculture (PA), aiming to provide a comprehensive understanding of their combined impact and mutually reinforcing relationship. Employing a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, we explore the synergies and transformative potential of integrating IoT and AI in agricultural systems. The review also aims to identify present trends, challenges, and opportunities in utilizing IoT and AI in agricultural systems. Diverse forms of agricultural practices are scrutinized to discern the applications of IoT and AI systems. Through a critical analysis of existing literature, this study contributes to a deeper understanding of how the integration of IoT and AI technologies can revolutionize PA, resulting in improved efficiency, sustainability, and productivity in the agricultural sector.

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Section: Deep Learningmentioning

confidence: 99%

Section: Impact Of Iot Weaknesses On Ai/ml Modelsmentioning

confidence: 99%